Kernel-based data modelling using orthogonal least squares selection with local regularisation

Combining orthogonal least squares (OLS) model selection with local regularisation or smoothing leads to efficient sparse kernel-based data modelling. By assigning each orthogonal weight in the regression model with an individual regularisation parameter, the ability for the OLS model selection to produce a very parsimonious model with excellent generalisation performance is greatly enhanced